Algorithmic decision support is rapidly becoming a staple of personalized
medicine, especially for high-stakes recommendations in which access to certain
information can drastically alter the course of treatment, and thus, patient
outcome; a prominent example is radiomics for cancer subtyping. Because in
these scenarios the stakes are high, it is desirable for decision systems to
not only provide recommendations but supply transparent reasoning in support
thereof. For learning-based systems, this can be achieved through an
interpretable design of the inference pipeline. Herein we describe an automated
yet interpretable system for uveal melanoma subtyping with digital cytology
images from fine needle aspiration biopsies. Our method embeds every
automatically segmented cell of a candidate cytology image as a point in a 2D
manifold defined by many representative slides, which enables reasoning about
the cell-level composition of the tissue sample, paving the way for
interpretable subtyping of the biopsy. Finally, a rule-based slide-level
classification algorithm is trained on the partitions of the circularly
distorted 2D manifold. This process results in a simple rule set that is
evaluated automatically but highly transparent for human verification. On our
in house cytology dataset of 88 uveal melanoma patients, the proposed method
achieves an accuracy of 87.5% that compares favorably to all competing
approaches, including deep "black box" models. The method comes with a user
interface to facilitate interaction with cell-level content, which may offer
additional insights for pathological assessment.

本文介绍了一种用于结膜黑色素瘤亚型识别的自动化且可解释的系统，该系统嵌入了候选细胞学图像的每个自动分割细胞作为由许多代表性幻灯片定义的 2D 流形中的一个点，从而为组织样本的细胞水平组成进行推理，为活检提供可解释的亚型识别，并且通过对畸变的 2D 流形进行分区，训练基于规则的幻灯片级分类算法来评估。

利用可解释的算法对全切片细胞学图像进行葡萄膜黑色素瘤亚型划分

An Interpretable Algorithm for Uveal Melanoma Subtyping from Whole Slide  Cytology Images

The model parameters of convolutional neural networks (CNNs) are determined
by backpropagation (BP). In this work, we propose an interpretable feedforward
(FF) design without any BP as a reference. The FF design adopts a data-centric
approach. It derives network parameters of the current layer based on data
statistics from the output of the previous layer in a one-pass manner. To
construct convolutional layers, we develop a new signal transform, called the
Saab (Subspace Approximation with Adjusted Bias) transform. It is a variant of
the principal component analysis (PCA) with an added bias vector to annihilate
activation's nonlinearity. Multiple Saab transforms in cascade yield multiple
convolutional layers. As to fully-connected (FC) layers, we construct them
using a cascade of multi-stage linear least squared regressors (LSRs). The
classification and robustness (against adversarial attacks) performances of BP-
and FF-designed CNNs applied to the MNIST and the CIFAR-10 datasets are
compared. Finally, we comment on the relationship between BP and FF designs.

本文提出了一种解释性前馈 (FF) 设计，通过数据统计推导出当前层的网络参数，构建了基于 Saab 变换和 LRS 级联的卷积与全连接层的 CNN， 并在 MNIST 和 CIFAR-10 数据集上比较了 BP 和 FF 设计的分类准确性和鲁棒性，并探讨了 BP 和 FF 设计之间的关系。